隨著自駕車技術的重視度日益提高，車道輔助的功能成為了其中不可或缺的一部分。而高準確度和效率是車道輔助系統中重要的考慮因素。在這種背景下，設計一個輕巧且準確的車道辨識系統變得尤為重要。本研究提出一個基於深度學習的車道線偵測模型架構，為了解決深度模型在初期特徵提取時常見的收斂性惡化問題，我們使用高效的特徵提取模塊，並對網路結構進行重新調整。且考慮到車道線細長的獨特性，以及在極端天氣或多變的道路環境下的辨識挑戰，我們引入了循環特徵轉移聚合器及多分支的解碼器，以更有效地傳遞車道線特徵，提升模型面對高挑戰性環境的泛化能力，最終模型在準確度及速度達到良好的平衡。本研究方法使用TuSimple車道線檢測資料集，以及自行蒐集的實驗數據和實驗車輛上進行了不同場景的驗證，與其他先進的車道線檢測模型比較後取得了優異的結果。

As the importance of autonomous driving technology continues to grow, the functionality of lane assistance has become an indispensable part of it. In particular, high accuracy and efficiency are crucial considerations in a lane assistance system. In this context, designing a lightweight and accurate lane recognition system has become particularly important.
This research proposes a lane detection model structure based on deep learning. To address the common problem of convergence deterioration during the initial feature extraction of deep models, we utilize an efficient feature extraction module and rearrange the network structure. Considering the thin and elongated uniqueness of lane, as well as the recognition challenges under extreme weather or variable road environments, we have introduced a Recurrent Feature-Shift Aggregator and multi-branch decoders to effectively convey lane features, enhancing the model's ability to generalize in high-challenge environments. The final model achieves a good balance between accuracy and speed.
This research method uses the TuSimple lane detection dataset, along with self-collected experimental data and experimental vehicles for verification in different scenarios. Compared with other advanced lane detection models, it yielded excellent results.

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